strategic multiscale framework for materials and industry

Strategic Multiscale Framework

A New Multiscale

Science - Engineering Environment

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Alessandro Formica

March 2014

Alessandro Formica, March 2014 All rights reserved

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TABLE OF CONTENTS

1. Multiscale and The Future of Technology Innovation, Engineering and Manufacturing... pag. 3

2. Strategic Multiscale Framework Architecture.. pag. 7

3. Integrated Multiscale Science - Engineering Framework.. pag. 9

3.1 Architecture. pag. 9

3.2 Multiscale Data, Information and Knowledge Analysis and Management Systempag. 10

3.3 Multiscale Science Engineering Information Space. pag. 18

3.4 The Information Driven Concept and Analysis Scheme... pag. 24

3.5 Multiscale Modeling & Simulation as Knowledge Integrators and Multipliers. pag. 27

3.6 Multiscale Multiresolution Multiphysics Testing, Experimentation and Sensing. pag. 31

3.7 Integrated Multiscale Science Engineering Analysis Strategies.. pag. 34 3.7.1 Methodologically Integrated Multiscale Science - Engineering Strategies pag. 34 3.7.2 Multiscale Science - Engineering Analysis Schemes. pag. 41

3.8 Designing the R&D and Engineering/Manufacturing Processes. pag. 44 3.8.1 R&D and Engineering/Manufacturing Process Architecture pag. 44 3.8.2 R&D and Engineering/Manufacturing Strategy Management System.. pag. 49 3.8.3 Integrated R&D and Engineering Analysis Strategies. pag. 51

4. Integrated Multiscale Science Engineering Technology, Product and Process Development (IMSE-TPPD) Framework.. pag. 54

4.1 IMSE-TPPD Architecture and Overview pag. 54

4.2 Multiscale Multidisciplinary Science Engineering Cyber Extended Enterprise Framework. pag. 55 4.3 Computer Aided R&D, Engineering and Manufacturing /Processing (CARDE-MP) Framework.... pag. 56 4.3.1 Architecture.. pag. 56 4.3.2 Multiscale Manufacturing and Processing.. pag. 57 4.3.3 Multiscale Environmental Monitoring and Impact Analysis..... pag. 61

4.4 Multiscale Science Engineering Virtual Testing pag. 66

4.5 Virtual Multiscale Innovative Technology and Systems Development Framework pag. 69 91

4.6 Virtual Multiscale Life Cycle Engineering Framework.. pag. 72

4.7 Multiscale Science Engineering Knowledge Integrator and Multiplier (KIM) . Computing Information Communication Infrastructural Framework....... pag. 74

About the Author.. pag. 77


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1. Multiscale and The Future of Technology Innovation, Engineering and Manufacturing

Computational Multiscale has become a key asset in the R&D and Engineering World and an important element for Technology, Products and Processes Innovation. Multiscale methods helped to establish a bridge between Science and Engineering and the related domains of knowledge. Continuous advances in Computational Methods (Virtual Environments) and High Performance Computing provided the basis to define a new vision of Multiscale we refer to as "Strategic Multiscale.

The term Strategic means that Multiscale Methodologies are applied not only to improve Modeling and Simulation Methods, but, also, to improve in a significant way R&D, Engineering and Manufacturing Organization, Structure and Strategies.

Complexity of Products and Manufacturing Technologies and the related R&D and Engineering Processes is continuously increasing: researchers and engineers have to manage, integrate and coordinate an ever widening spectrum of analytical, computational, experimental, testing and sensing models, methods and techniques.

A Fundamental Goal of the Strategic Multiscale Framework is to address this Challenge outlining a set of new concepts, methods and environment to Design the R&D and Engineering Process

A Distinguishing Element of the Strategic Multiscale Framework is the new concept of Multiscale Modeling and Simulation as Knowledge Integrators and Multipliers and Unifying Paradigm for Scientific and Engineering Domains.

This new methodological Science - Engineering Framework allows us to give a New Dimension and Meaning to the term Virtual as far as Engineering and Manufacturing are concerned. and introduce a New Field: Virtual Technology Innovation, which is the connection element between Science and Engineering/Manufacturing Domains.

The Strategic Multiscale Framework defines a Comprehensive Theoretical and Methodological Environment to design and implement a New Generation of Virtual Science Based Technology Innovation, Engineering and Manufacturing Strategies where Multiscale Modeling and Simulation become Pivotal Elements of the R&D and Engineering Manufacturing World overcopming classical divisions between the Computational and the Experimental, Testing and Sensing Areas.

A Fundamental Characteristics of the Strategic Multiscale Framework is to allow for a smooth, continuous, efficient, structured and timely transfer of scientific knowledge inside the Technology Development, Engineering and Manufacturing Processes and related Computational Frameworks.

The concept of Multiscale as Unifying Paradigm is not new. In the mid of ninenties, several researchers in the Chemical Engineering Field (Sapre and Katzer, Leou and Ng, and Villermaux) and the author of this document (Alessandro Formica) highlighted the need of a comprehensive Multiscale approach as a key Strategy to establish a new Unifying Paradigm in order to enable a better correlation between scientidfic and engineering advances and related knowledge domains. Later on, Prof. Charpentier, past European Federation of Chemical Engineering President highlighted again the strategic relevance of this conceptual scheme.


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The definition of new Frameworks like the Integrated Computational Materials Engineering (ICME) and the Integrated Compuitational Materials Science Engineering (ICMSE) ones and the launch of the US Presidential Materials Genome Initiative (MGI) put the bases for a wide Industrial Application of Multiscale Science Engineering Integration Strategies and Frameworks

The European Union FET FLAGSHIP Human Brain Preoject (HBP) is a demonstration of the Strategic Value of Multiscale Science Engineering Integration. To fully understand processes and related relationships characterizing Brain Functions and Functionalities over the whole range of scalea and Brain organization levels and the fundamental relationships with diseases, new (Multiscale) Computational, Experimental and Data Analysis Methodologies, Techniques and Strategies will be developed and applied. New (Multiscale) Methodologies will also be functional to develop a New Generation of (Multilevel) Non Von Neumann Computing Systems.Engineering and Manufacturing are quickly changing. Science has already become a key issue and value for both the fields and this trend will become increasingly important in the coming years . Many Projects have clearly demonstrated that, today, is possibile to use Multiscale (Science Engineering) Computational Methodologies to Design and Manufacture new inherently Hierarchical Multiscale Materials, Devicews, Components and Systems (Nano To Macro Integration).

Fig. 1 Multiscale (Nano To Macro) System Design (MIT)

Multiscale as Unifying Paradigm for Chemical Engineering

Prof. Charpentier, past European Federation of Chemical Engineering (EFCE) President, at the 6th World Congress of Chemical Engineering - Melbourne 2001, described his Vision of Multiscale as Strategic Paradigm for Chemical Engineering. We report his words: One key to survival in globalization of trade and competition, including needs and challenges, is the ability of chemical engineering to cope with the society and economic problems encountered in the chemical and related process industries. It appears that the necessary progress will be achieved via a multidisciplinary and time and length multiscale integrated approach to satisfy both the market requirements for specific end use properties and the environmental and society constraints of the industrial processes and the associated services.

This concerns four main objectives for engineers and researchers:

(a) total multiscale control of the process (or procedure) to increase selectivity and productivity,

(b) design of novel equipment based on scientific principles and new methods of production: process intensification,

(c) manufacturing end-use properties for product design: the triplet processus-product-process engineering,

(d) implementation of multiscale application of computational modeling and simulation to real-life situations: from the molecular scale to the overall complex production scale.


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Multiscale Science Engineering Integration implies that we can not only define a Science Driven Engineering, but, also a Engineering Driven Science. Multiscale Computational Methodologies (Virtual Engineering and Manufacturing) should consider the impact of these global trends over their development, structure and related implementation strategies in order to define their Future.

It is to be highlighted that the awarding of the Nobel Prize in Chemistry to three scientists for the development of Multiscale Models for Complex Chemical Systems has helped to create the optimal intellectual and scientific context to introduce high level Projects and Initiatives in the Multiscale Science Engineering Integration field

Strategic Multiscale Framework Goals:

Defining a New Organization and Structure of the R&D and Engineering World: Designing the R&D and Engineering Process Architecture

Defining a New Frontier for Virtual Worlds and Application Strategies: Virtual Multiscale Science Based Technology Innovation, Engineering and Manufacturing

Easing knowledge transfer between the different stages of the R&D and Engineering/Manufacturing process

Integrating and Strcuturine Data, Information and Knowledge from the Scientific and Engineering Worlds

Defining new cooperation and partnering schemes among academy, research, and industry. In the new science-engineering context, engineering can become an important driver for science, overturning historic relationships and dependencies and putting the bases for a new way of doing science and engineering. Not only advances in science can be stimulated and driven by technology progress and the need to solve specific technological and engineering problems, but research strategies will be more and more influenced by technology roadmaps and vice versa.

Putting the bases to define a new structure and organisation for the research and industrial world based, from an Infrastructural point of view, on a new generation of Multiscale Multidisciplinary Science Engineering Cyberinfrastructures and, from a methodological point of view, on the here described Framework to bridge the gap between disciplines and the different scientific and engineering approaches.

Enabling new Technological Engineering Solutions (Multiscale Engineering: From Multiscale Analysis to Multiscale Design). New Frameworks enable the design of inherently Hierarchical Multiscale Systems (materials, structures components, products and processes) which is a fundamental condition to fully exploit in the industrial environment the potentialities of Nano and Micro Technologies.

In such a context it is possible to realize a real fusion between

science-driven engineering and engineering-driven science

which represents a key goal of the Strategic view of multiscale.

The Royal Swedish Academy of Sciences has awarded the Nobel Prize in Chemistry for 2013 to Martin Karplus of Universit de Strasbourg, France and Harvard University, Cambridge, MA, USA; Michael Levitt of Stanford University School of Medicine, Stanford, CA, USA; and Arieh Warshel of the University of Southern California, Los Angeles, CA, USA "for the development of multiscale models for complex chemical systems


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General References

David L. McDowell, Jitesh H. Panchal, Hae-Jin Choi. Carolyn Conner Seepersad, Janet K. Allen, Farrokh Mistree, 2010. Integrated Design of Multiscale, Multifunctional Materials and Products - Published by Elsevier .

Oden , J.T. , Belytschko , T. , Fish , J. , Hughes , T.J.R. , Johnson , C. , Keyes , D. , Laub , A. , Petzold , L. , Srolovitz , D. , Yip , S. , 2006 . Simulation-based engineering science: Revolutionizing engineering science through simulation . In : A Report of the National Science Foundation Blue Ribbon Panel on Simulation-Based Engineering Science . National Science Foundation : Arlington, VA .

Olson , G.B. 1997 . Computational design of hierarchically structured materials . Science, 277 ( 5330 ) , 1237 1242 .

Alessandro Formica. Fundamental R&D Trends in Academia and Research Centres and their Integration into Industrial Engineering Report drafted on behalf of European Space Agency, July 2000

Alessandro Formica, Multiscale Science Engineering Integration A New Frontier for Aeronautics, Space and Defense, Italian Association of Aeronautics and Astronautics (AIDAA), March 2003


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2. Strategic Multiscale Framework Architecture

The theoretical and methodological basis of the Strategic Multiscale Framework is constituted by the following key elements:

The extension of the Model concept to the Experimentation, Testing and Sensing Fields giving a new meaning to the Virtual Engineering and Manufacturing concept and approach. In this context, a new Vision of Multiscale Modeling & Simulation as Knowledge Integrators and Multipliers and Unifying Paradigm for Scientific and Engineering Methodologies and Knowledge Domains has been defined. Multiscale Modeling and Simulation integrate the full spectrum of science and engineering methodological approaches and knowledge environments. This new Vision puts Computational Frameworks and High Performance Computing at the center of the R&D and Engineering/Manufacturing World even more than classical Virtual concepts and approaches.

The Multiscale Science-Engineering Information Space concept to integrate data, information and knowledge from computational models and methods and experimental, testing and sensing models and techniques to develop, validate and apply Computational Models. Uncertainty Quantification (UQ) and Quantification of Margin of Uncertainties (QMU) have become critical issues as the relevance of Modeling and Simulation is continuously increasing.

The Information Driven Analysis concept and scheme which, together with the Science Engineering Information Space concept is a key element to shape Integrated R&D and Engineering/Manufacturing Analysis and Design Strategies, following the Multiscale Modeling and Simulation as Knowledge Integrators and Multipliers concept and application environment.

New Multiscale Science Engineering Data, Information and Knowledge Management Systems based upon the Multiscale Maps concept

New Multiscale Methods to Model, Simulate and Design the Technology Development and Engineering/Manufacturing Processes and Products Life Cycle: Virtual Multiscale Innovative Technology and Systems Development Framework and Virtual Multiscale Life - Cycle Engineering Framework and Environmental Impact Analysis

Basic Conceptual, Theoretical and Methodological Framework

Integrated Multiscale Science Engineering Framework

Integrated Multiscale Science Engineering Technology, Product

and Process Development

(IMSE-TPPD) Framework

Analysis And Design of a New Generation of Materials, Devices Systems, and related Manufacturing Processes


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The Strategic Multiscale Framework embodies the following Elements:

Integrated Multiscale Science Engineering Framework Described in the Chapter 3 - which represents the theoretical, conceptual and methodological core. Key Elements: Multiscale Data, Information and Knowledge Analysis and Management System Multiscale Science Engineering Information Space Multiscale Modeling & Simulation as Knowledge Integrators and Multipliers Multiscale Multiresolution Experimentation, Testing and Sensing Methodologically Integrated Multiscale Science Engineering Strategies The Information Driven Concept Multiscale Science - Engineering Analysis Schemes R&D and Engineering/Manufacturing Process Architecture R&D and Engineering Analysis and Design Strategy Management System Integrated R&D and Engineering Analysis Strategies

Integrated Multiscale Science Engineering Technology, Product and Process Development (IMSE-TPPD) Application Framework - Described in the Chapter 4 - Multiscale Multidisciplinary Science Engineering Enterprise Framework

Computer Aided R&D, Engineering and Manufacturing/Processing (CARDE-MP) Framework which implements the Integrated Multiscale Science Engineering Framework

Multiscale Manufacturing and Processing Multiscale Environmental Monitoring and Impact Analysis

Multiscale Science Engineering Virtual Testing Virtual Multiscale Innovative Technology and Systems Development Framework

Virtual Multiscale Life Cycle Engineering Framework

The Multiscale Knowledge Integrator And Multiplier Computing, Information and Communication (CIC) Infrastructural Framework

A distinguishing characteristics of the Strategic Multiscale Framework is that it can incorporate and take advantage of a wide range of existing Software Environments in many areas: from Data Analysis, Workflow Management, Statistics, Graphics, Single and Multiscale Computational Codes to name a few.


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3. Integrated Multiscale Science - Engineering Framework

3.1 Architecture

Main elements of the Conceptual and Methodological Framework are:

Multiscale Science - Engineering Data, Information and Knowledge Analysis and Management System

Multiscale Science Engineering Information Space

Information Driven Multiscale Science Engineering Analysis Concept and Schemes

Multiscale Modeling & Simulation as Knowledge Integrators and Multipliers and Unifying Paradigm for Scientific and Engineering Methodologies and Knowledge Domains The role of Multiscale as Unifying Paradigm and Language for Science and Engineering was discussed by Alessandro Formica, some years ago in the book - Computational Stochastic Mechanics In a Meta-Computing Perspective December 1997 - Edited by J. Marczyk pag. 29 Article: A Science Based Multiscale Approach to Engineering Stochastic Simulations.

Multiscale Multiresolution Multiphysics Testing, Experimentation and Sensing

Methodologically Integrated Multiscale Science Engineering Methodologies

New Methods, Tools and Strategies to Design the R&D and Engineering Analysis Process

Integrated Multiscale R&D and Engineering Analysis Strategies


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3.2 Multiscale Science Engineering Data, Information and Knowledge Management System

A critical issue for a wide diffusion of the science based engineering analysis and design approach in the industrial field is the availability of Software Environments (CAD/CAE/CAM/CAP) specifically conceived for multiscale science engineering strategies and applications. Today, notwithstanding the growing diffusion of multiscale inside university, research, and even industry, software environments (CAD/CAE/CAM/CAP) specifically conceived to implement multiscale science-engineering integration visions and strategies are still in their starting phase. The lack of software environments specifically conceived to implement a multiscale science-engineering integration strategy represents a fundamental hurdle to a large scale implementation of multiscale inside innovative technology development and engineering/manufacturing/processing fields. The new Data, Information and Knowledge Management System proposed in this Document rests on the concepts of:

Multiscale Multiphysics Multiresolution Maps

Multiscale Multi Abstraction Level Knowledge Domains

The Multiscale Multiresolution Maps here described is an extension of the Map concept discussed by Alessandro Formica in the Multiscale Science Engineering Integration: A new Frontier for Aeronautics, Space and Defense White Book published on March 2003 by Italian Association of Aeronautics and Astronautics.

Multiscale Multiresolution Maps are Multiscale Multiresolution Information and Knowledge Structures describing complex networks of relationships and interdependencies between a large spectrum of Information Variables characterizing Systems Structure and Dynamics. Relationships and interdependencies between Information Variables are worked out applying several mathematical techniques such as multivariate analyses and neural networks [available inside a specific Data Analysis Module. This Module correlates sets of Data to Data Sources, Tasks and Integrated Strategy Maps] to raw data coming from a wide range of Data Sources (analytical and computational models, data bases, experimentation, characterization, testing and sensing). covering the full spectrum of scales (from atomistic to macro) and the full spectrum of disciplines. Multiscale Maps structure Data and turn Data into Information. Maps are organized in a hierarchical way: A Map can incorporate a set of lower level Maps. For instance: a Multiscale Physical Map linked to a specific Process (Hypervelocity Impact, Combustion or Explosion, for instance) can be constructed by assembling a range of Multiscale Physical Maps describing more elementary physical (chemical and biochemical) phenomena (fracture, fragmentation, phase change,..) related to a specific material or component of a System. Accordingly any Element represented inside a Map can be decomposed into more elementary Elements. Representations can be static and dynamic. Multiscale Maps incorporate error analyses and uncertainty quantification methods. Multiscale Maps make an extensive use of Multiscale Multiresolution Static and Dynamic Graphic Representations.

Multiscale Multi Abstraction Knowledge Domains are a further organization level is represented by Knowledge Domain which are Structures that can aggregate several Maps related to one or more scales of the same typology or of different typologies related to the same or different operational conditions, analysis and design hypotheses and solutions. Maps can be set up and integrated inside a Knowledge Domain applying several aggregation and clustering schemes. Knowledge Domains can be organized in a Hierarchical Way. Knowledge Domains can be related to specific R&D and Engineering/Manufacturing Tasks and Phases. They can track Knowledge structure and organization as we transition from a R&D and Engineering Phase and Task to another one.

Multiscale Maps and Multiscale Knowledge Domains allow for an effective insertion and management of the more fundamental knowledge (basic and applied research) inside the sequence of Technology Development and Engineering phases. At each step/phase of the R&D and Engineering Process , Multiscale Maps and Knowledge Domains are built taking full advantage of the knowledge get in the previous step/phase.


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Several typologies of Maps are foreseen which describe relationships among variables, structures and processes:

A) R&D And Engineering (for systems of any kind of complexity)

Multiscale Analysis and Design Variable Maps tracking relationships between Analysis and Design Variables . Multiscale Analysis and Design Variable Maps are built applying available knowledge and current working hypotheses at the drafting time; statistical analysis schemes (multivariate, PCA) or other techniques like neural networks to data coming from several sources: data bases, computation, analytical theories, experimentation, testing, sensing. Data integration and fusion techniques are applied to reconcile and integrate data coming from different sources characterized by a range of accuracy and reliability degrees. Multiscale Analysis and Design Variable Maps describe relationships between variables and parameters used to characterize Systems Behaviour over a full range of space and time scales and disciplines and over a range of operational conditions..

Multiscale Physics Maps identifying the Physical, Chemical and Biochemical Phenomena and Processes considered fundamental to carry out a specific task and describe relationships and interdependencies among them. The following table illustrates a textual version of a simplified Physics Map:

Multiscale Architectural/Structural Maps describing relationships between the hierarchy of Sub-Systems, Components, Devices, Materials and Elementary Structures constituting an Engineering System (or System of Systems) of arbitrary level of complexity. This kind of Maps incorporates a special set of Elements referred to as Interfaces which describe interconnections among Architectural/Structural constituents inside a scale and among different scales.

Engineering System Multiscale Monitoring and Control Maps describing (Hierarchical) Networks of Sensors and Control Devices and Systems and their relationships with Elements to be monitored and controlled (described in the Multiresolution Multiscale Architectural/Structural Maps). Transformation Processes induced by control actions are described thanks to Multiresolution Multiscale Physics Maps and Multiresolution Multiscale Architectural/Structural Maps. This kind of Maps describes the quantities monitored and controlled, time and space monitoring and control resolution, sensing and control devices characteristics and operational schemes

Multiscale Materials (Tantalum) Characterization (Livermore) Physics Map

Atomistic length-scale modeling input: interatomic potentials (calculated with quantum mechanics) output: dislocation generation, motion and interaction with other defects scale physics: properties of individual defects (dislocations, vacancies, interstitials, dopants), defects mobility, diffusion, clusters, surface reactions

Microscale length scale modeling input: dislocation generation, motion and interaction with other defects output: yield and hardening rules for single crystals scale physics : defect interactions, precipitates, dislocation reactions, the early stages of void growth, grain boundaries and the interactions between dislocations and grain boundaries

Mesoscale Modeling input: yield and hardening rules for single crystals output: mesoscale models of polycrystal aggregates (100s of grains) scale physics : shear band, dislocation walls, collective dynamics of microstructure, interface diffusion, grain coarsening, recrystallization, crack growth, fracture

Mesoscale Homogenization / Continuum Model input: mesoscale models of polycrystal aggregates (100s of grains) output: pressure and strain path dependent yield surface for continuum code hardening. scale physics : polycrystal plasticity, temperature fields, hydrodynamic motion, textures, microstructures homogenization, anisotropic hardening.


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Multiscale Functional Maps describing relationships between Engineering System Architectural/Structural Elements and Functions performed

Multiscale Requirements - Performance Property Structure Maps describing relationships between Requirements, Performance, Structural Elements and related Properties over the whole scales and representation levels.

Multiscale Performance Property Structure Manufacturing/Processing Maps describing the impact of Processing techniques over the network of Performance - Structure - Property relationships over the whole scales and representation levels.

Fig. 2 Physics Map Example (from Overview of the Fusion Materials Sciences Program Presented by S.J. Zinkle, Oak Ridge National Lab Fusion Energy Sciences Advisory Committee Meeting February 27, 2001 Gaithersburg)

This figure depicts a Information Structure like the proposed Multiscale Physics Maps. In this case the Multiscale Physics Map describes relationships between physical phenomena and chemical/physical structural transformations linked to Radiation Damage Process for Metals

A cluster of Multiscale Physics Maps, linked to specific physics phenomena or processes, architectural element and operational conditions, can define what can be called a Physical (Chemical and Biochemical) Phenomena and Processes Knowledge Domain. Knowledge Domains are managed by the Multiscale Science Engineering Data, Information and Knowledge Management System.


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Multiscale Multilevel Architectural and Structural Maps

Any System of arbitrary degree of complexity (an air transportation system, an energy production system, an aerospace vehicle, a chemical plant, a structure, a nanotechnology device, a nanostructured material), can be recursively broken down in a set of simpler (macro, meso, micro, nano and atomistic) Architectural and Structural Elements and Interface/Interconnection Elements. Interconnections and Integration develop along two lines: Horizontal (same scale) and Vertical (different scales). We distinguish two kinds of Systems: Technological Systems and Natural Systems where the Technological System (or System of Systems) operates.

Fig. 3 Two dimensional multilevel multiscale view of an aircraft. (from the Validation Pyramid and the failure of the A-380 wing Presentation given by I. Babuska (ICES, The University of Texas at Austin), F. Nobile (MOX, Politecnico di Milano, Italy), R. Tempone (SCS and Dep. of Mathematics, Florida State University, Tallahassee) in the context of the Workshop Mathematical Methods for V&V SANDIA , Albuquerque, August 14-16, 2007

Three new features distinguish this kind of Maps and related Multiscale Multilevel Science Engineering CAD Systems:

Multiscale Multilevel Architectural/Structural Element Networks Analysis and Description. New CAD Systems should describe the full set of multiscale multilevel (inside a single scale) Architectural and Structural Elements of a System (or System of Systems) - including the Operational Environment - and related interconnections. Interconnection Elements describe two way interactions between Elements. This feature is of particular importance for System Engineering analyses and if we like to assess the impact of the System upon the environment where it operates and the effects of the Environment on the System for the whole Life Cycle and the whole spectrum of operational conditions including extreme ones and accidents.

Zooming and Selected Multilevel Multiscale view capabilities. Users should have the possibility to select a full spectrum of views at different levels of resolution, scales and abstraction ways. Multiple views should be visualized in order not to lose connections among different levels of abstraction, resolution and scales. The zooming function should allow users to transition from a levels of abstraction, levels and scales in an interactive way.

Multi Abstraction Levels: we can select groups (clusters) of architectural/structural elements of different typologies over a spectrum of scales and resolution levels as needed to carry out specific analyses and design tasks.


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This kind of Maps gives a comprehensive picture of the:

Architectural and Structural and Interface/Interconnection Elements (from macro to atomic levels as needed) which constitute a system and its related Horizontal and Vertical organization: from the System (or System of Systems) down to elementary structures (atoms/molecules, groups of atoms and molecules)

Materials, Energy, Chemical and Biochemical Substances Flow (pollutants emitted toward the Natural System for instance,) among the Elements constituting the System or System of Systems

Analysis and Design Variables their relationships and interdependencies and links between Analysis and Design Variables and Architectural and Structural Elements

Properties of the full set of Architectural and Structural Elements

Performance and Requirements for the full set of Architectural and Structural Elements. Performance are calculated and/or measured during the R&D and Engineering Process while, Requirements are defined and refined by designers.

Architectural and Structural Maps evolve along the Technology Development and Engineering Analysis and Design Process thanks to Analysis and Design Modules and Strategy Modules. Maps are built using the available knowledge; as analysis and design activities proceed, they are interactively modified. Different Maps can be linked to different Architectural Hypotheses and Decisions for different purposes and tasks during the R&D and Engineering Process. Maps are recorded, organized and managed in specific Architectural and Structural Map Data Bases. Architectural and Structural Elements Maps are related to: Functional Maps

Monitoring and Control Maps

Physics Process Maps

Multiscale Monitoring and Control Maps

This kind of Maps gives a comprehensive picture of the Multiscale Multilevel Networks of Monitoring and Control Devices and Systems their interconnection schemes and their functionalities and operational modes. Multiscale Monitoring and Control Maps are related to: Architectural and Structural Maps

Physics Maps (Physical and Bio-Chemical Phenomena and Processes Monitored and effects of Control actions)

Multiscale Functional Maps

We define two types of Functional Maps. The first one, which can be called Direct Functional Map, describes Functions carried out by the

System and the full hierarchy of its Elements. Direct Functional Maps link Architectural/Structural Elements to Functions and they describe what functions are performed by Architectural/Structural Elements.

The second one, which can be called Inverse Functional Map relates Functions to Architectural/Structural Elements over the full spectrum of hierarchy levels

Functional Maps are linked to:

Architectural and Structural Maps

Physics and Processes Maps

Functional Maps defined during the Technology Development and Engineering Process are recorded, organized and managed by specific Functional Maps Data Bases. Maps are indexed in such a way as to relate them to specific R&D and Engineering Phases and Tasks.


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Multiscale Multiphysics Maps

We use the term Physics to indicate a more or less complex cluster of elementary physical and biochemical phenomena/processes occurring inside a scale or developing over a spectrum of scales. Phenomena/Processes are, for instance, failure, stress corrosion cracking erosion, phase transformation, A Process can be broken down in a full hierarchy of more elementary Processes and Phenomena. The distinction between processes and phenomena is, to some extent, arbitrary. It is a matter of opportunity. Phenomena and Processes can concern more Architectural/Structural Elements.

Physics Maps are linked to:

Architectural/ Structural and Functional Maps.

Monitoring and Control Maps

Requirements - Performance Property Structure Maps

Performance Property Structure - Processing Maps

Multiscale Manufacturing Systems any level of the hierarchy - Operational Modes - Environmental Emission Maps

Physics Maps are software environments which describe :

the full set of physical (biological and chemical, as needed) phenomena and processes which rule the dynamics of Architectural/Structural Elements (Interconnection Elements included) of a System under analysis/design for a specific Task and their interactions inside a scale and over different scales.

The full hierarchy of (geometrical, physical and biochemical) Architectural/Structural transformations related to a specific set of Phenomena/Processes linked to a specific R&D and Engineering Task .

Relationships between the full hierarchy of processes, phenomena and Architectural/Structural transformations for a specific Task

Maps are indexed in such a way as to relate them to specific R&D and Engineering Phases and Tasks.

Physics Maps are linked to Multiscale Methodologically Integrated Strategy Maps described in the Paragraph 3.8.1. Multiscale Methodologically Integrated Strategy Maps describe what Computational Models, Experimentation, Testing and Sensing Techniques/Procedures are applied to analyze specific physical phenomena/processes and their interconnection networks, sequence of execution and data. Physics Maps are built using the available knowledge, as R&D and Engineering proceed, they are interactively modified.

Physics Maps defined during the R&D and Engineering Process are recorded, organized and managed by specific Physics Maps Data Base.

Integration of the previously defined Multiscale Maps allow to correlate:

functions to physical phenomena and processes (linking Multiscale Functional Maps with Multiscale Physics Maps

Properties (Multiscale Architectural/Structural Maps) to Physics (Multiscale Physics Maps)


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Multiscale Performance Properties Structure Processing Maps

The definition of the Performance Properties Structure Processing relationships has become a cornerstone of the modern Materials Science and Engineering and R&D and Engineering at all. Prof. Gregory Olson, Northwestern University has been one of the pioneers of this strategy. Prof. Olson described this approach in a Science Magazine article: Vol. 277 (29 August 1997) pp. 1237-1242.

Fig. 4 (from Questek) illustrates the application of a Performance Properties - Structure Processing Map to the design of new alloys.

Performance Properties Structure - Processing Maps are indexed in such a way as to relate them to specific R&D and Engineering, Phases and Tasks. Performance Properties - Processing Structure Maps defined during the R&D and Engineering Process for different purposes and tasks are organized and recorded in the Performance Properties - Structure - Processing Map Data Bases The Multi Abstraction Level feature of the Maps can be seen in the figure: each box is a specific abstraction level. Each Box refer to a cluster of processes occurring over u spectrum of scales and resolution levels.

This kind of software environments contribute to characterize and manage relationships between processing and manufacturing activities and the resulting architecture/structures

These Maps identify :

defects (typology, physical and chemical characteristics, density and distribution : statistical and deterministic analysis) linked to specific processes and manufacturing activities and steps

bio chemical and structural features and transformations linked to specific processes and manufacturing conditions, procedures and technologies

This kind of Maps are related to Multiscale Physics Maps


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B) Manufacturing and Processing

Multiscale Manufacturing and Processing Systems Architectural, Functional and Monitoring & Control Maps describing: the (multiscale/multilevel) architecture (hierarchical networks of units [from Plants to Cell,

Robots and Machines/Tools] at different scales and complexity levels of any kind of Manufacturing/Processing Systems and related interconnections and interdependencies (material flow). At the highest abstraction level, a Unit can represent a whole Manufacturing/Processing Systems incorporating several Plants and other Elements. The representation scheme is recursive: A Unit can be decomposed into a network of simpler Units, a simpler Units can be, in turn, be decomposed into other networks of even more elementary Units over the whole hierarchy of scales and representation levels, as needed.

the full spectrum of functions carried out by the units constituting Manufacturing/Processing Systems and their relationships and interdependencies. Multiscale Physics Maps and Multiscale Structural Maps are applied to describe physical, chemical and bio-chemical transformations and processes occurring at and over the full spectrum of Units.

the (Hierarchical) networks of multiscale monitoring and control (M&C) devices and systems over the full spectrum of scales and levels This kind of Maps describes the quantities monitored and controlled, time and space resolution, sensing and control devices characteristics and operational schemes

the (Hierarchical Network) of Inspection Systems, their Functions and Operational Modes

Multiscale Multilevel Manufacturing Processes Execution Flow: this kind of Maps describes, for each specific Manufacturing Process of any level of complexity the execution flow (manufacturing/process sequence of steps) throughout the full set of Plants. Process Units, Cells, Machines/Robots,., the work performed at each step, the characteristics of the Unit, the structural/chemical/physical transformations worked out (also using Architectural/Structural Maps and Physics Maps), the inspections performed, the materials flow..

Multiscale Manufacturing Systems any level of the hierarchy - Operational Modes - Environmental Emission Maps Theses Maps represent a new Generation of Maps specifically conceived to evaluate the impact on the Environment of Manufacturing/Processing Systems for a wide range of operational conditions and design solutions. Maps describe relationships among Manufacturing System (any level), its Operational Modes and related Emissions (any kind).

Multiscale Maps represents a key element of a new Multiscale Computer Aided Research, Development, Engineering (CARDE-MP) Software Systems. Main objectives:

Developing new schemes allowing for a more in-depth analysis and structuring of data, information and knowledge and related correlations and interdependencies

Integrating the full spectrum of Data Sources (Data Bases, Analytical Theories, Computational Models, Experimentation , Testing and Sensing). The Information Space and the Modeling and Simulation as Knowledge Integrators and Multipliers concepts and methods can ease this kind of Integration

Developing new CAD/CAE/CAM/CAP Environments specifically conceived to Design and Produce new Hierarchical Multiscale Nano To Macro Multifunctional Systems in the context of an Integrated Science Engineering Approach

Multiscale Maps are indexed and related to specific R&D and Engineering Tasks and Phases, Design Hypotheses and Design Decisions and Operational Conditions.

The Multiscale Science Engineering Data, Information and Knowledge Management System records, organizes and manages all the previously defined Maps and Knowledge Domains (Item A and Item B). Each Map and Knowledge Domain is characterized by a set of Tags which link it to a specific task, phase and operational conditions and analysis and design hypothesis inside the R&D and Engineering/Manufacturing Analysis and Design Process.


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3.3 Multiscale Science - Engineering Information Space

This concept was presented by Alessandro Formica in the Report Fundamental R&D Trends in Academia and Research Centres and Their Integration into Industrial Engineering (September 2000), drafted for European Space Agency (ESA). The Multiscale Science-Engineering Information Space is associated to any analytical, computational model/method, and experimental, testing and sensing procedure and technique applied to a specific task. The Multiscale Science-Engineering Information Space defines:

what spectrum of information about physical/biological/chemical phenomena and processes at what level of accuracy and reliability (Uncertainty Quantification (UQ) and Quantification of Margin

of Uncertainty (QMU))

can be get by a computational model or experimental/testing/sensing technique/procedure applied in a specific context for a specific task.

A set of model variables characterize analytical and computational models. A set of method variables characterize the specific method applied to perform simulations. A set of system variables characterizes the system to be modeled and simulated or subjected to experimental, testing and sensing analyses. A set of experimental, testing and sensing variables characterizes experimental, testing and sensing techniques and procedures.

The Science Engineering Information Space also applies to cluster of computational models and experimental/testing/sensing techniques/procedures linked through multiscale multiphysics coupling schemes. In this case we can define coupling scheme parameters which describe the method used to couple models and/or experimental/testing/sensing techniques/procedures.

With the term system we refer to the system (materials, device, component,.) under analysis.. A set of variables describe the geometrical, biological, chemical and physical structure of the system.

With the term Operational Environment, we refer to External Fields and Loading Conditions

With the term model we refer to the mathematical/computational representation of the system under investigation. A set of variables characterize and describe the models (boundary conditions, external fields/loading conditions, space and time dimensions, discretization techniques, particles number and typology,.). In the proposed framework we extend the concept of Model to the Experimental/Testing/Sensing world as explained in the Paragraph 2.4

With the term method we refer to the specific deterministic and statistical analytical and computational method (Monte Carlo. Classical Molecular Dynamics, Quantum Molecular Dynamics, Density Functional Theory, Dislocations Dynamics, Cellular Automata,).

With the term experimental/testing/sensing technique and procedure variables we refer to the variables which describe technical characteristics of the experimental and testing apparatus and the specific operational modes and conditions (globally referred to as procedure)

Information Space Construction To build the Information Space of a specific (single scale or multiscale) computational model with reference to a specific system and analysis task (fracture, delamination, oxidation,), we perform a set of simulations, varying in a systematic way parameters/variables which characterize the physical (chemical and biochemical) phenomena/processes of interest in the context of a specific task including external forces. Then, we validate computational models using a set of experiments, tests and sensing measures to track the boundaries of the Information Space and evaluate accuracy and reliability (Uncertainty Quantification UQ). Information Spaces can be built also for experimental, testing and sensing techniques and procedures. In this case a Cross Validation strategy is applied which foresee the comparison of a spectrum of experimentation, testing and sensing techniques.

The next page Box synthetically describes the key role and significance played by new Verification & Validation Strategies (Uncertainty Quantification and Quantification of margin of Uncertainty) for the Computational field.


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The Predictivity and Validation Issues

The National Nuclear Security Program (NNSA), in the context of the Advanced Simulation and Computing (ASC) Initiative, established the Predictive Science Academic Alliance Program (PSAAP) focusing on the emerging field of predictive sciencethe application of verified and validated computational simulations to predict the behavior of complex systems where routine experiments are not feasible. The goal of these emerging disciplines is to enable scientists to make precise statements about the degree of confidence they have in their simulation-based predictions. Five PSAAP Centers have been created: California Institute of Technology: Center for the Predictive Modeling and Simulation of High-Energy Density Dynamic Response of Materials; Purdue University: Center for Prediction of Reliability, Integrity and Survivability of Microsystems (PRISM); Stanford University: Center for Predictive Simulations of Multi-Physics Flow Phenomena with Application to Integrated Hypersonic Systems; University of Michigan: Center for Radiative Shock Hydrodynamics (CRASH); University of Texas at Austin: Center for Predictive Engineering and Computational Sciences (PECOS)

The following text, drawn from the Presentation Can Complex Material Behavior be Predicted? Given by Prof. Michael Ortiz, Caltech PSAAP Center Director, at the DoE NNSA Stockpile Stewardship Graduate Fellowship Program Meeting Washington DC, July 14, 2009, illustrates objectives and approach underlying the general PSAAP Strategy and Methodology concerning Validation and Predictivity challenges:

PSAAP Caltech High-Energy-Density Dynamic Response of Materials (Hypervelocity Impact Application Field) Center objective: rigorous certification of complex systems operating under extreme conditions. l

Overarching Center objectives: Develop a multidisciplinary Predictive Science methodology focusing on high-energy-density

dynamic response of materials Demonstrate Predictive Science by means of a concerted and highly integrated experimental,

computational, and analytical effort that focuses on an overarching ASC-class problem: Hypervelocity normal and oblique impact at velocities up to 10km/s

Overarching approach: A rigorous and novel Quantification of Margin of Uncertainty (QMU) methodology will drive

and closely coordinate the experimental, computational, modeling, software development, verification and validation efforts within a Yearly Assessment format

Two issues deserve to be highlighted:

The central role of the Uncertainty Quantification and Quantification of Margin of Uncertainty issues in the context of the Computational Models Validation effort to shape R&D and Engineering activities. This vision can be, to some extent, related to the previously illustrated concepts: Multiscale Science Engineering Information Space, Range of Validity and Information Driven R&D and Engineering Strategy

The key role of Computational, Analytical and Experimental Efforts Integration. New (multiscale) experimental techniques and analytical (theoretical) developments are fundamental to develop and apply new and more powerful (predictive) computational models and strategies. The Vision is in line with our Methodologically Integrated R&D and Engineering approach


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The Information Space, should also include Multiscale Analysis and Design Variable and Multiscale Physics Maps worked out during the previously described construction process.

It is possible to apply different schemes to build the Information Space for a specific task. For instance:

fixing model and methodology variables and varying external conditions and/or system variables (typology and architecture of a material or device)

fixing external conditions and system variables and varying model and/or methodology variables (for a molecular dynamics model: simulation time, force fields typology, number of particles,).

any other possible combinations

The Information Space, for each specific computational model/method (or cluster of models: multiscale multiphysics) applied to a specific task includes information about the computing resources needed to perform simulations and the experimental, testing and sensing techniques used to validate it

Information Space Relevance

Three considerations underlie the definition of the Multiscale Science Engineering Information Space concept and method:

rationally correlating advances for models/methods and multiscale multiphysics coupling schemes with the capability of getting information thought to be important to carry out specific R&D and Engineering/Manufacturing tasks.

rationally defining the role of models/methods and related multiscale multiphysics coupling schemes inside a more general R&D and Engineering analysis and design process and the interdependencies among different models, methods, techniques and coupling schemes.

formally tracking and planning the development path (roadmap) for models, methods, techniques and related coupling schemes as linked to specific R&D and Engineering analysis and design tasks, and assessing the relative importance of the different models/methods and related coupling schemes to get some Information at a specific level of accuracy and reliability.

We can consider an aerodynamic design task, for instance. The ability to run a 30/50-million grid points Navier Stokes simulation in the same lapse of time, or less, as a 1-million grid points simulation, is surely an important result from an engineering analysis and design point of view. But, what is the relative weight between model dimension and physics (turbulence) modeling as function of a particular task (calculation of aerodynamic coefficients, for instance) at a certain level of accuracy and reliability?

In this way, can we get more reliable and accurate information instrumental to reducing cost and development time and introduce innovative technological solutions? The answer is not so straightforward. Turbulence plays a key role in flow dynamics phenomena of critical importance for the design of a wide range of systems. Suppose the biggest simulation model used the same turbulence model (or a slight modification) as the one employed in the smallest one, what is the relationship among the number of grid points, turbulence modeling (model variables) and the capacity of getting the needed engineering information at the right level of accuracy (for instance : CP - CL or vortex dynamics look at the V-22 vortex ring state story) ? Is the number of grid points or the turbulence modeling the dominant knowledge factor from a designer point of view?

The situation becomes even more critical when the physics and chemistry to be taken into account are highly complex (aerothermodynamics and combustion, for example). It is sufficient to think at a combustion chamber or an hypersonic vehicle. Several variables such as complex thermo chemical phenomena, the interaction between turbulence and chemistry, multiphase and phase change phenomena, condition the information space linked to a model.

We introduce, now, the Range of Validity concept for the Multiscale Science-Engineering Information Spaces associated to models/methods and experimental, testing and sensing techniques and procedures.


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Range of Validity is the range of the Multiscale Science-Engineering Information Space inside which we can get a set of information from specific models/methods and experimental, testing and sensing procedures/techniques and possible coupling schemes at a certain level of accuracy and reliability (uncertainty quantification). It is of fundamental relevance to determine how the Range of Validity changes as model, method, experimental & testing and coupling scheme variables change. The range of validity is a key element to determine (for a specific task) :

how good computational models and experimental, testing and sensing techniques and coupling schemes should be to get Information we think to be needed to carry out a task at a predefined error and uncertainty level.

how to define the right mix of computational models/methods and experimental & testing procedures/techniques and coupling schemes to get what we think to be the right information at the right level of accuracy and uncertainty to perform a specific R&D and Engineering analysis and design task..

Fig. 5 (Center for Computational Materials Design NSF) describes a framework to define in a formal way the Range of Validity (or Applicability Domain) of a model

The Multiscale Science-Engineering Information Space formalizes what, today, is being performed in an empirical and semi-empirical way. Such a formal procedure allows us to rigorously evaluate the relative weight of the several model/method/technique variables as function of the Information Space and the best research/development paths for computational models/methods and experimental & testing techniques to address specific challenges.

The Multiscale Science-Engineering Information Space concept and method enables researchers and designers to jointly define development roadmaps for computational models and experimental, testing and sensing techniques.


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The need of defining the Information Space associated to computational method and experimental techniques, in the context of the Verification & Validation process, has been analyzed, for instance, by Tim Trucano in Uncertainty in Verification and Validation: Recent Perspective Optimization and Uncertainty Estimation, Sandia National Laboratories Albuquerque, NM 87185-0370 SIAM Conference on Computational Science and Engineering, February 12-15, 2005, Orlando, Florida - SAND2005-0945C.

Fig. 6 The figure (from the previously quoted document) illustrates the Information Space concept

Thanks to the Multiscale Science Engineering Information Space concept and method, it is possible to define Costs/Benefits Function for models/methods and related coupling schemes as referred to different Technology Development and Engineering tasks. Benefits are referred to the Information get and Costs to the resources needed to develop, validate and apply models/methods/techniques/coupling schemes. This kind of Function could be useful to Technology Development and Engineering Project Managers to better manage and allocate human, organizational and financial resources.

The Multiscale Science Engineering Information Space and the Range of Validity concepts can be related with new Verification and Validation (V&V) strategies and methods. Uncertainty Quantification (UQ) is a key challenge for Computational Science and Engineering. UQ and Quantification of Margin of Uncertainty (QMU) [performance (measured) vs. requirements (set)] , are becoming (have already become) one of the new driver and objective for the Computational World. The Predictive Science Academic Alliance Program (PSAAP) managed by US National Nuclear Security Agency (NNSA) is a clear example of application of these statements.

The Multiscale Science-Engineering Space is of fundamental importance to define and implement Methodologically Integrated Multiscale Science-Engineering Strategies which foresee the coherent use of several different single and multiscale computational models and methods, and several different single and multiscale experimental, testing and sensing techniques working over a full range of scales.

The Multiscale Science Engineering Information Space is becoming of increasingly importance for Science and Engineering because for a specific tasks is common using a spectrum of computational models and a spectrum of experimental techniques and methods. Integration calls for rigorous methodologies to determine what kind of Information can be get from computations and what from experimentation, testing and sensing.


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According to the previous analysis, the Multiscale Science-Engineering Information Space concept and method is instrumental to identify:

shortcomings and limitations of computational models/methods and related multiscale multiphysics coupling schemes for specific R&D and Engineering tasks

development lines (roadmaps) for computational models and methods and multiscale coupling schemes to achieve specific R&D and Engineering objectives

shortcomings and limitations and development lines (roadmaps) for experimental, testing and sensing techniques and procedures and related multiscale multiphysics coupling schemes

integrated roadmaps for jointly developing multiscale multiphysics analytical, computational and (multiscale) experimental, testing and sensing techniques to deal with specific R&D and Engineering Tasks

integrated strategies for jointly applying multiphysics multiscale analytical, computational and (multiscale) experimental, testing and sensing techniques/procedures to deal with specific R&D and Engineering Tasks


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3.4 The Information Driven Concept and Analysis Scheme

The relevance of Information, as a key element to shape R&D and Engineering Strategies, is winning an increasing attention. Several studies have been performed, for instance, by Jitesh H. Panchal, Janet K. Allen, David L. McDowell and colleagues at Georgia Institute of Technology. Alessandro Formica highlighted the role of Information to drive modeling and simulation strategies in the White Paper HPC and the Progress of Technology : Hopes, Hype and Reality published in US by RCI Ltd on February, 1995. In this document he discussed the concept of Engineering Information Analysis. The issue was also dealt with in the context of the Accelerated Insertion of Materials (AIM) Program (1999) managed by US DARPA. The following text is drawn from DARPA Proposer Information Pamphlet BAA 00-22 clearly describes the theme and related challenges:

The need for an Information-Driven strategy . .There are many interrelated technical challenges and issues that will need to be addressed in order to successfully develop new approaches for accelerated insertion. These include, but are not limited to, the following: The construction of the designers knowledge base: What information does the designer need and to what fidelity? How does one coordinate models, simulations, and experiments to maximize information content? What strategies does one use for design and use of models, computations, and experiments to yield useful information? How can redundancies in the data be used to assess fidelity ? The development/use of models and simulation: What models are required to be used and/or developed in the context of the designer knowledge base? How can models of different time and length scales be linked to each other and to experiments? How can the errors associated with model assumptions and calculations be quantified? How can models be used synergistically with experimental data ?

The use of experiments: Are there new, more efficient experimental approaches that can be used to accelerate the taking of data? How can experiments be used synergistically with models? How can legacy data and other existing data base sources be used ?

The mathematical representation of materials: How can one develop a standardized mathematical language to: describe fundamental materials phenomena and properties; formulate reliable, robust models and computational strategies; bridge interfaces; and identify gaps between models, theory and experimental materials science and engineering? How can this representation be used to develop hierarchical principles for averaging the results of models or experiments while still capturing extremes ?

In the context of the Integrated Multiscale Science Engineering Framework, Information is a key element which, to a large extent, drives and shapes R&D and Engineering/Manufacturing Strategies.

The term Information Driven means that R&D and Engineering/Manufacturing strategies for specific Tasks have to address what can be called The Information Challenge for R&D and Engineering :

What Information at what level of accuracy and reliability (uncertainty quantification) is needed to accomplish a task

What Relationships and Interdependencies between analysis and design variables should be tracked over a full range (as needed) of space and time scales to accomplish a task

What kind of information sources (analytical, computational, experimental, testing and sensing models/techniques) are needed and how they can be combined to get the previously identified information


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Accordingly, the following key issues define the The Information - Driven Analysis Scheme for R&D and Engineering/Manufacturing:

Select a set of scales and resolution levels (the choice is not unique and it is related to a specific Phase and Task)

identify physical phenomena, geometry and variables at the different space and time scales which influence the dynamics of a system at the reference scale at a certain level of accuracy and fidelity (different scenarios for accuracy and fidelity can be taken into account).

identify at a qualitative and quantitative level relationships and interdependencies among phenomena, geometry, equations and variables at the different scales

assess how and to what extent (qualitative and quantitative evaluation) the capability of getting information thought to be needed to describe the dynamics of the system at the reference scale at a certain level of accuracy and fidelity is affected by the spectrum of phenomena at the other scales.

assess how requirements defined at a scale determine and affect requirements to the other scales The definition of how information and requirements propagate in a qualitative and quantitative way (in a deterministic and/or probabilistic fashion and taking into account uncertainties) from a scale to another scale, from a resolution level to another resolution level, is a key step to :effectively deal with physics as well as with system and process complexity

Assess what Information at what level of accuracy and reliability is thought to be needed to accomplish a R&D and Engineering task . Thought to be needed means that the process is iterative, we start with some hypotheses and just Multiscale Science Engineering Strategies and related Data, information and Knowledge Analysis schemes and tools give us the possibility to improve evaluation about the Information needed to execute the task. Example : What Information (what physical and chemical phenomena and processes related to materials, structures and chemically reacting flows and their interactions) at what level of accuracy and uncertainty should we know to analyze the dynamics of a Thermal Protection Systems of an Hypersonic Vehicle for a specific operational environment?

Evaluate what physical length scales and related physical and biochemical phenomena rule the dynamics of the system under analysis for a specific Tasks, what is the relative weight, what are relationships and interdependencies between phenomena and processes inside a scale and between different scales (to be described thanks to Multiscale Maps).

Evaluate what Information at what level of accuracy and reliability can existing analytical, computational models, experimental, testing and sensing techniques and related coupling scheme give us (to be described using the Multiscale Science Engineering Information Space).

Assess what characteristics (Information Spaces) should new models/techniques and related coupling schemes have

Assess what combination of old and new computational, analytical, and experimental/testing methodologies at different levels of scale and resolution do we need to get the right information at the right level of accuracy and completeness for the different tasks in the different R&D and design stages. A critical step for the rational design of the R&D and engineering processes is a proper selection, integration, and sequencing of computational and analytical models and experimental/testing methodologies with varying degrees of complexity and resolution. To do that we have to define the Science-Engineering Information Space associated to each methodology.

Assess how good analytical and computational models, experimental, testing and sensing techniques and related coupling schemes should be to get the previously identified information thought to be needed to accomplish a task. How good means evaluating how much physical realism should be incorporated into the models and what scales hierarchy has to be taken into account. Not in all the cases, of course, we really need complex multiscale methodologies going down to the Schrdinger equations: simple single scale models can be accurate and reliable enough.


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Note: This kind of Information is critical to evaluate what new analytical and computational models and what new experimental, testing and sensing procedures/techniques should be developed and integrated to deal with a specific analysis task. It is important to identify not only what we know, but, in particular, what we do not know, what we should know, how we should know it (what combination of scientific and engineering methodologies and technologies should be needed). In this context, the lack of Knowledge becomes and important element to guide Strategies.

Furthermore, another very critical issue is that we need a rational approach to link advances in the different methods at the different scales with the new information we need to meet challenges in the different tasks in the different stages of the R&D and engineering process. How do we effectively and timely evaluate the impact of scientific methodological and information advances at an atomic, molecular, and grain (for materials) level on new technological and engineering solutions if we do not have conceptual and methodological (multiscale) frameworks to link methods and information at the different scales: from atomic to continuum? The Multiscale Science-Engineering Information Space can represent a first step to deal with these critical issues. If we like to shape new cooperative schemes between industry, from one side, and academia and research, from the other side, we have to define specific methodologies to evaluate the industrial and technological value of new scientific methodological advances.

It is to be highlighted that this Analysis Scheme is adaptive and iterative. It should be carried out at the starting time of any R&D and Engineering/Manufacturing Phase and Task using available data, information and knowledge and formulating hypotheses: Results get during the execution of a Phase and related Tasks will provide data, information and knowledge that allow to update and improve the Analysis Scheme and initial Hypotheses Phase after Phase, Task after Task.

The Information-driven approach is a fundamental element to assess if, where, when and to what extent we have to go down along the hierarchy of scales. Not in all the cases, of course, we should go down until Schrodinger equations from the continuum. Dont Model Bulldozers with quarks (Goldenfeld and Kadanoff, 1999)


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3.5 Multiscale Modeling and Simulation as Knowledge Integrators and Multipliers and Unifying Paradigm for Scientific and Engineering Methodologies and Knowledge Domains

The Vision of Multiscale Modeling & Simulation as Knowledge Integrators and Multipliers (KIM) and Unifying Paradigm for Scientific and Engineering Knowledge Domains and (Experimentation, Testing and Sensing) Methodologies characterizes the Integrated Multiscale Science-Engineering Framework and it represents the conceptual context inside which the Framework is applied to R&D and Engineering Processes. The KIM notion was presented by Alessandro Formica in the: HPC and the Progress of Technology : Hopes, Hype, and Reality RCI. Ltd Management White Paper February 1995

Multiscale Multiphysics Modeling and Simulation can be regarded as Knowledge Integrators and Multipliers (KIM) and Unifying Paradigm for Scientific and Engineering Knowledge Domains and Methodologies because Multiscale Models are able to integrate and synthesize, in a coherent framework, Data, information, and Knowledge from:

a number of disciplines,

a wide range of scientific and engineering time and space domains,

multiple scientific and engineering models (science-engineering integration) linked by a spectrum of coupling schemes.

a wide spectrum of Computational, Experimentation, Testing and Sensing Multiscale Science Engineering Data and Information Spaces built during the development, validation, application and improvement phases of the same Multiscale Models

several Maps generated by a wide range of methodologies (analytical theories, computation, experimentation, testing and sensing) during the development, validation, application and improvement phases of the same Multiscale Models

In this context, we propose to extend the concept of Model to include not only its mathematical formulation, but, also, Information Spaces and Maps linked to it for specific tasks. We also extend the concept of Model from the Computational to the Experimental, Testing and Sensing World

This Vision give a New Dimension to the Virtual Engineering and Manufacturing Concept and Strategy and Science Engineering Integration Methodologies and Environments open the way to define a New Field: Virtual Technology Innovation

Multiscale Information Spaces and Multiscale Maps embody and organize Data, Information and Knowledge get by the full spectrum of analytical theories, a set models at different scales and the related experiments, tests and sensing measures used to develop, validate and improve them. It is to be highlighted that all the existing Modeling and Simulation concepts, application strategies and methodologies, such as Virtual Prototyping , Simulation - Based Design, Simulation - Based Acquisition, Simulation Based Engineering Science (SBES) and Virtual Engineering, can be considered as particular cases of this more general concept and strategy.

We would like to emphasize that the KIM concept puts Multiscale Modeling and Simulation and, accordingly, HPC, at the centre of the R&D and Engineering/Manufacturing Processes much more than the classical Virtual Engineering and Manufacturing and Simulation Based Engineering Science concepts. Multiscale Modeling and Simulation become a key element to shape complex (multi and single scale) Experimental, Testing and Sensing Strategies.

The concept of Model as Knowledge Integrator is certainly not new. This view, in the mid of nineties, was clearly described in the chemical engineering field by James H. Krieger, in the article Process Simulation Seen As Pivotal In Corporate Information Flow - Chemical & Engineering News, March 27, 1995. The text reported the following statement of Irving G. Snyder Jr., director of process technology development, Dow Chemical : "The model integrates the organization. It is the vehicle that conveys knowledge from research all the way up to the business team, and it becomes a tool for the business to explore different opportunities and to convey the resulting needs to manufacturing, engineering, and research." . In the same article other companies such as BNFL and Du Pont expressed similar points of view.


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Note: Continuous advances in computational modeling and computing power makes it possible to build computational models which simulate the experimental or testing apparatus, the system to be probed and related interactions. This kind of modeling is an interesting asset to plan experimentation, testing and sensing and analyze results.

Key element of the KIM Vision is the extension of the concept of Model to the Experimental, Testing and Sensing World as detailed in the following:

The Concept of Experimental, Testing and Sensing Model

In the proposed theoretical and methodological framework it is necessary to extend the concept of Model from the Computational to the Experimental, Testing and Sensing World. In the context of the Experimental, Testing and Sensing World, for Model, as referred to a specific Experimental, Testing, Sensing activity carried out with specific techniques, working in a specific operational mode and probing a specific system for a specific task, we mean an Information and Knowledge Structure that define:

Characteristics (structure, composition, initial dynamics state, boundary conditions, external loadings) of the System to be probed

Characteristics of the equipment in terms of resolution, scale, physical and biochemical phenomena which can be probed

Characteristics of the specific Experimental, Testing and Sensing operational conditions and modes applied for specific R&D and Engineering Tasks

The Multiscale Science Engineering Information Space related to it

Multiscale Physics Maps .

As in the Computational World, it is easy to define the concept of Multiscale Experimental, Testing and Sensing Model. In this case the Information/Knowledge Structure refers to a cluster of different equipments and it embodies information about:

Interaction schemes among the different equipments

Data and Information Flow among the different equipments

Multiscale Computational Modeling and Multiscale Experimentation Integration Materials Research Society Bulletin

An important recognition of the key strategic relevance of the development of multiscale experimental techniques and their integration with multiscale computational modeling comes from the article Three-Dimensional Materials Science: An Intersection of Three-Dimensional Reconstructions and Simulations (Katsuyo Thornton and Henning Friis Poulsen, Guest Editors), published in the Materials Research Society (MRS) Bulletin June 2008.

..For example, by combining a nondestructive experimental technique such as 3D x-ray imaging on a coarse scale, FIB-based 3D reconstruction on a finer scale, and 3D atom probe microscopy at an even finer scale, one has an opportunity to capture materials phenomena over six orders of magnitude in length scale. This will bring materials researchers closer to the ultimate dream of a direct validation of multiscale models, both component by component and ultimately as an integrated simulation tool. In conjunction with the advances on the modeling side, such comprehensive experimental information is seen as very promising for establishing a new generation of models in materials science based on first principles..


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Even if attention to the integration issue is positively increasing, particularly for models development and verification and validation phases, there are still conceptual and methodological relationships not thoroughly examined between challenges and advances in modeling and simulation, and progress and challenges in experimental, testing and sensing techniques. Experience is showing us that ever more complex and large scale computations call for increasingly sophisticated and expensive experimental/testing/sensing techniques both in the model development, validation and improvement phases. Advances in modeling and simulation are intimately linked to progress in experimental, testing and sensing methods and techniques and vice versa. A direct correlation and strong mutual dependencies, in the model development, validation and improvement phases, exist between the two fields sometimes regarded as antithetic. It is important to take into account that, if computational methods and computing technologies are continuously progressing, also experimental, testing and sensing techniques are making continuous significant progress.

It is sufficient to think at the impact on materials research that the Scanning Tunneling Microscopy (STM) and Atomic Force Microcopy (AFM) techniques have had.

It is advisable to consider a joined development of new Computational Methods and Strategies with new Experimentation, Testing and Sensing Development Techniques and Strategies and vice versa.

Furthermore more and more complex and powerful 3D and 4D experimental, testing and sensing techniques increasingly call for complex computational models to interpret, analyse and organize data and define integrated measurement and characterization strategies. A priority target is to develop a unified conceptual context to synergistically take advantage of advances in both the fields and not only for the computational models development and validation phases, as it occurs today, but, also, in the application phase. All of that in the context of Integrated Frameworks and Strategies An effective R&D and Engineering Strategy should find the way to synergistically take advantage of advances in both the fields. In several cases, today, advanced HPC/Modeling/Simulation and experimental/testing/sensing programs are conceived and managed as separated realities. This situation can lead to costs increase and hamper and limit the effectiveness of both the programs. The new Vision reconcile development streams and roadmaps in the two fields.

In the R&D and Engineering Process, today and, more and more, in the future, we have to integrate a full spectrum of (interdependent and interlinked) scientific and engineering models and codes with a wide spectrum of experimental, testing and sensing (scientific and engineering) data with a full spectrum of scientific and engineering analytical formulations. Data get from experimentation, testing and sensing covers several physical and biochemical disciplines and domains and several different space and time scales. It is clear that, increasingly, we have to deal with very complex interaction patterns intra the experimentation, testing and sensing world, intra the computational modeling world and inter the experimentation, testing, sensing and computational modeling worlds. Multiscale Science Engineering Information Spaces, Multiscale Maps and the Kim vision can be a first step to realize this integration. The KIM concept is a fundamental theoretical and methodological basis. Methodologically Integrated Multiscale Science - Engineering Strategies are built upon it. Classical Modeling & Simulation Application Strategies in the innovative technology development field are significantly hampered and limited the following fundamental contradiction: when we develop innovative technologies and innovative engineering solutions, we often enter a territory where theories are not well developed and reliable, and the availability of experimental and testing data is fragmented or lacking at all. Accordingly, we face a fundamental and intrinsic problem: Modeling & Simulation is the reference strategy to limit risks, costs, and development times by heavily reducing the resort to complex and expensive experimental and testing activities. However, contrary to what happens in the mature or evolutionary technology environment, we cannot adopt this strategy because we still need very significant experimental and testing activities to develop and validate the needed computational models. That is what is called a classical Catch 22 situation: (i.e.) a situation which involves intrinsic contradictions.


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This contradiction is certainly not ignored. In the presentation Modeling and Simulation in the F-22 Progr

strategic multiscale framework for materials and industry

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